The placement of advertisements and promotions have traditionally relied on extrapolation of a small data panel of viewing behavior and other demographic information from research companies, such as the Nielsen Company, to inform these placements. These panels are limited to robustly measuring the exposure of fairly wide, mostly demographically-based audience targets, such as “women age 25-54”, and reporting is generally limited to an aggregate metric for each program aired or the commercial minutes aired within a program.
Planning and placing TV advertising using these metrics alone, however, introduces significant inefficiencies into the placement process. In general, advertisers have a narrower, more specific target than the wide demographic measure produced by data panels. For example, a health cereal manufacturer may be more interested in reaching “women who are health conscious shoppers” instead of “women age 25-54”, but present systems are unable to distinguish between these two groups, or at least narrow the scope of the broader demographic. In addition, advertisers establish reach and frequency targets for their advertising campaigns, but current metrics and systems are unable to distinguish which viewers the advertisements are being delivered to in each advertising placement, and thus many spot placements are simply wasted by exposing viewers who have already seen the advertisement many times while other viewers, who are part of the target, go unexposed. Such methods further lack comprehensive placement and optimization algorithms to help advertisers choose available advertising slots to more effectively reach their target audiences.